Hearth vs Skywork

Hearth and Skywork are both ai agent apps tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Hearth

Hearth runs on your own hardware and handles the tasks that usually demand a SaaS subscription: opening applications, reading and writing files, driving a real browser you can watch, and carrying memory of past sessions — all without a single request leaving your network. The MIT license means you can fork it, extend it, and ship modified versions without legal friction. That said, the GitHub repo shows 9 stars and 297 commits from a single-org project, which signals early-stage software rather than a hardened production runtime. Windows is the primary target; Linux and macOS support is not confirmed by the page. Teams that need cross-platform deployment or enterprise support will hit the ceiling fast.

Skywork

Skywork deploys what it calls Super Agents — task-specialized agents that handle discrete output types including documents, slides, spreadsheets, podcasts, and video — so a single research prompt can fan out into multiple finished formats without manual reformatting. The vendor states citations are embedded in outputs, which addresses the verification problem that makes generic AI drafts unusable in analyst and academic workflows. The free tier runs on a daily credit cap, so high-volume or back-to-back generation tasks hit a ceiling fast. There is no self-hosted option, which rules out any team with data residency requirements. Teams doing complex conditional branching across agent steps will find the platform's current surface area constraining.

Attribute	Hearth	Skywork
Pricing	Free	Paid
Price	—	$19.99/month (Pro plan)
Free trial	No	No
Open source	Yes	No
Has API	No	No
Self-hosted option	Yes	No
Platforms	Windows (primary); macOS/Linux from source	Web, iOS, and Android, Windows Desktop
Released	—	2025-05
Pros	Fully local execution with no telemetry or account requirement, which means sensitive file operations and internal automation never leave the machine — eliminating the data-residency risk that blocks cloud tools in regulated environments. MIT license with a self-hosted architecture, so you can fork, modify, and redistribute without licensing negotiation — the thing that stops most teams from customizing a SaaS automation tool at all. Voice and natural-language input connected directly to OS-level actions, so non-technical users can run repetitive file and app tasks without writing scripts or maintaining a workflow canvas. Reusable, installable 'skills' that the community can share, which means automation one developer builds for cleaning a downloads folder can be packaged and reused by anyone on the same stack — no rebuild from scratch. A visible, watchable browser session rather than headless automation, so you can audit exactly what the agent is doing in real time instead of debugging a black-box scraper after it goes wrong.	Multi-modal Super Agents handle discrete output types — documents, slides, sheets, podcasts, video — in a single workflow, so you avoid the manual reformatting loop that eats hours after every research pass. The vendor states outputs include citations, which means analysts and academics get a deliverable they can actually defend, rather than a fluent draft they have to re-source from scratch. Task-specialized agent architecture means each output type has a dedicated agent rather than a single generalist, so domain-specific formatting conventions are more likely to hold across output types. Free tier entry point with daily credits lets a team validate the agent's output quality against their specific use case before committing budget — avoiding the scenario where you discover the tool breaks on your content type after a paid contract. End-to-end workflow design — from research query to finished deliverable — means the handoff between research and production is handled inside the platform, reducing the number of tools a team has to coordinate.
Cons	The project targets Windows explicitly; the page does not confirm Linux or macOS support. Teams running mixed-OS environments or deploying to Linux servers cannot use Hearth without forking the codebase and porting the OS-control layer themselves — at which point they are maintaining their own tool, not adopting one. At single-digit GitHub stars and a single-org contributor base, there is no meaningful community to surface bugs, maintain compatibility with OS updates, or keep pace with new local model releases. When a Windows update breaks the file-control layer, the fix timeline depends entirely on one maintainer. There is no multi-user, logging, or audit-trail architecture described anywhere in the repo. Teams that need to demonstrate who ran what automation and when — for compliance, for incident review, or for shared-machine safety — will find nothing here and will move to a tool like Open Interpreter paired with structured logging, or a managed RPA platform, before the first audit request arrives.	The daily credit cap on the free tier blocks any realistic production workflow: a consultant running three or four research-to-deck tasks in a morning exhausts the allocation before lunch, forcing a choice between upgrading or stopping work mid-sprint. No self-hosted option exists. Any team operating under data residency requirements, healthcare data rules, or enterprise security policies that prohibit third-party cloud processing cannot use the platform at all — they move to a self-hostable alternative regardless of output quality. Complex agent coordination — branching based on what one agent returns before triggering the next — is not described as a configurable capability on the vendor's current surface. Teams that need conditional logic across agent steps are building that layer themselves outside the platform. The platform launched publicly in May 2025, meaning production reliability data, edge-case failure documentation, and community-reported workarounds are thin. Teams making a tooling decision with a six-month roadmap are betting on a product with a short public track record.

Bottom line

Hearth is free while Skywork is paid; Hearth is open source. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Hearth and Skywork?

Hearth is Free and open source, while Skywork is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Hearth better than Skywork?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.